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@book{fielddsr22026,
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@book{wickhamDataScience2017,
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@book{wickhamAdvanced2014,
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}

 @Manual{here,
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 @article{Mair_Wilcox_2019, title={Robust statistical methods in R using the WRS2 package}, ISSN={1554-3528}, url={https://doi.org/10.3758/s13428-019-01246-w}, DOI={10.3758/s13428-019-01246-w}, abstractNote={This paper introduces the R package WRS2 that implements various robust statistical methods. It elaborates on the basics of robust statistics by introducing robust location, dispersion, and correlation measures. The location and dispersion measures are then used in robust variants of independent and dependent samples t tests and ANOVA, including between-within subject designs and quantile ANOVA. Further, robust ANCOVA as well as robust mediation models are introduced. The paper targets applied researchers; it is therefore kept rather non-technical and written in a tutorial style. Special emphasis is placed on applications in the social and behavioral sciences and illustrations of how to perform corresponding robust analyses in R. The R code for reproducing the results in the paper is given in the Supplementary Materials.}, journal={Behavior Research Methods}, author={Mair, Patrick and Wilcox, Rand}, year={2019}, month={May} }

 @article{Wilcox_1994, title={The percentage bend correlation coefficient}, volume={59}, ISSN={1860-0980}, DOI={10.1007/BF02294395}, abstractNote={A well-known result is that the usual correlation coefficient,ρ, is highly nonrobust: very slight changes in only one of the marginal distributions can alterρ by a substantial amount. There are a variety of methods for correcting this problem. This paper identifies one particular method which is useful in psychometrics and provides a simple test for independence. It is not recommended that the new test replace the usual test ofH0:ρ = 0, but the new test has important advantages over the usual test in terms of both Type I errors and power.}, number={4}, journal={Psychometrika}, author={Wilcox, Rand R.}, year={1994}, month={Dec}, pages={601–616} }

 @misc{robinsonBroomConvertStatistical2019,
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}

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}

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@article{tang_ggfortify_2016,
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  journal = {The R Journal},
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@article{interactions,
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@article{makowski_compute_2020,
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@article{makowski_modelbased_2020,
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  institution = {{Working Papers in Economics and Statistics, Research Platform Empirical and Experimental Economics, Universit\"at Innsbruck}},
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@article{lenth_emmeans_2020,
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@article{morey_bayesfactor_2018,
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@book{fox_r_2019,
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}

